Machine Learning Predicts Accurately Mycobacterium tuberculosis Drug Resistance From Whole Genome Sequencing Data

Combating antimicrobial resistance in malaria, HIV and tuberculosis

Antimicrobial resistance poses a significant threat to the sustainability of effective treatments against the three most prevalent infectious diseases: malaria, human immunodeficiency virus (HIV) infection and tuberculosis. Therefore, there is an urgent need to develop novel drugs and treatment protocols capable of reducing the emergence of resistance and combating it when it does occur. In this Review, we present an overview of the status and underlying molecular mechanisms of drug resistance in these three diseases. We also discuss current strategies to address resistance during the research and development of next-generation therapies. These strategies vary depending on the infectious agent and the array of resistance mechanisms involved. Furthermore, we explore the potential for cross-fertilization of knowledge and technology among these diseases to create innovative approaches for minimizing drug resistance and advancing the discovery and development of new anti-infective treatments. In conclusion, we advocate for the implementation of well-defined strategies to effectively mitigate and manage resistance in all interventions against infectious diseases.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine-learning approaches

Background

Pyrazinamide is one of four first-line antibiotics used to treat tuberculosis; however, antibiotic susceptibility testing for pyrazinamide is challenging. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, encoding an enzyme that converts pyrazinamide into its active form.

Methods

We curated a dataset of 664 non-redundant, missense amino acid mutations in PncA with associated high-confidence phenotypes from published studies and then trained three different machine-learning models to predict pyrazinamide resistance. All models had access to a range of protein structural-, chemical- and sequence-based features.

Results

The best model, a gradient-boosted decision tree, achieved a sensitivity of 80.2% and a specificity of 76.9% on the hold-out test dataset. The clinical performance of the models was then estimated by predicting the binary pyrazinamide resistance phenotype of 4027 samples harbouring 367 unique missense mutations in pncA derived from 24 231 clinical isolates.

Conclusions

This work demonstrates how machine learning can enhance the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs.

Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes

The advent of rapid whole-genome sequencing has created new opportunities for computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data. Both rule-based and machine learning (ML) approaches have been explored for this task, but systematic benchmarking is still needed. Here, we evaluated four state-of-the-art ML methods (Kover, PhenotypeSeeker, Seq2Geno2Pheno and Aytan-Aktug), an ML baseline and the rule-based ResFinder by training and testing each of them across 78 species-antibiotic datasets, using a rigorous benchmarking workflow that integrates three evaluation approaches, each paired with three distinct sample splitting methods. Our analysis revealed considerable variation in the performance across techniques and datasets. Whereas ML methods generally excelled for closely related strains, ResFinder excelled for handling divergent genomes. Overall, Kover most frequently ranked top among the ML approaches, followed by PhenotypeSeeker and Seq2Geno2Pheno. AMR phenotypes for antibiotic classes such as macrolides and sulfonamides were predicted with the highest accuracies. The quality of predictions varied substantially across species-antibiotic combinations, particularly for beta-lactams; across species, resistance phenotyping of the beta-lactams compound, aztreonam, amoxicillin/clavulanic acid, cefoxitin, ceftazidime and piperacillin/tazobactam, alongside tetracyclines demonstrated more variable performance than the other benchmarked antibiotics. By organism, Campylobacter jejuni and Enterococcus faecium phenotypes were more robustly predicted than those of Escherichia coli, Staphylococcus aureus, Salmonella enterica, Neisseria gonorrhoeae, Klebsiella pneumoniae, Pseudomonas aeruginosa, Acinetobacter baumannii, Streptococcus pneumoniae and Mycobacterium tuberculosis. In addition, our study provides software recommendations for each species-antibiotic combination. It furthermore highlights the need for optimization for robust clinical applications, particularly for strains that diverge substantially from those used for training.

TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations

The most widely practiced strategy for constructing the deep learning (DL) prediction model for drug resistance of Mycobacterium tuberculosis (MTB) involves the adoption of ready-made and state-of-the-art architectures usually proposed for non-biological problems. However, the ultimate goal is to construct a customized model for predicting the drug resistance of MTB and eventually for the biological phenotypes based on genotypes. Here, we constructed a DL training framework to standardize and modularize each step during the training process using the latest tensorflow 2 API. A systematic and comprehensive evaluation of each module in the three currently representative models, including Convolutional Neural Network, Denoising Autoencoder, and Wide & Deep, which were adopted by CNNGWP, DeepAMR, and WDNN, respectively, was performed in this framework regarding module contributions in order to assemble a novel model with proper dedicated modules. Based on the whole-genome level mutations, a de novo learning method was developed to overcome the intrinsic limitations of previous models that rely on known drug resistance-associated loci. A customized DL model with the multilayer perceptron architecture was constructed and achieved a competitive performance (the mean sensitivity and specificity were 0.90 and 0.87, respectively) compared to previous ones. The new model developed was applied in an end-to-end user-friendly graphical tool named TB-DROP (TuBerculosis Drug Resistance Optimal Prediction: https://github.com/nottwy/TB-DROP ), in which users only provide sequencing data and TB-DROP will complete analysis within several minutes for one sample. Our study contributes to both a new strategy of model construction and clinical application of deep learning-based drug-resistance prediction methods.

Machine learning and phylogenetic analysis allow for predicting antibiotic resistance in M. tuberculosis

BACKGROUND

Antimicrobial resistance (AMR) poses a significant global health threat, and an accurate prediction of bacterial resistance patterns is critical for effective treatment and control strategies. In recent years, machine learning (ML) approaches have emerged as powerful tools for analyzing large-scale bacterial AMR data. However, ML methods often ignore evolutionary relationships among bacterial strains, which can greatly impact performance of the ML methods, especially if resistance-associated features are attempted to be detected. Genome-wide association studies (GWAS) methods like linear mixed models accounts for the evolutionary relationships in bacteria, but they uncover only highly significant variants which have already been reported in literature.

RESULTS

In this work, we introduce a novel phylogeny-related parallelism score (PRPS), which measures whether a certain feature is correlated with the population structure of a set of samples. We demonstrate that PRPS can be used, in combination with SVM- and random forest-based models, to reduce the number of features in the analysis, while simultaneously increasing models' performance. We applied our pipeline to publicly available AMR data from PATRIC database for Mycobacterium tuberculosis against six common antibiotics.

CONCLUSIONS

Using our pipeline, we re-discovered known resistance-associated mutations as well as new candidate mutations which can be related to resistance and not previously reported in the literature. We demonstrated that taking into account phylogenetic relationships not only improves the model performance, but also yields more biologically relevant predicted most contributing resistance markers.

Machine Learning of the Whole Genome Sequence of Mycobacterium tuberculosis: A Scoping PRISMA-Based Review

Tuberculosis (TB) remains one of the most significant global health problems, posing a significant challenge to public health systems worldwide. However, diagnosing drug-resistant tuberculosis (DR-TB) has become increasingly challenging due to the rising number of multidrug-resistant (MDR-TB) cases, despite the development of new TB diagnostic tools. Even the World Health Organization-recommended methods such as Xpert MTB/XDR or Truenat are unable to detect all the Mycobacterium tuberculosis genome mutations associated with drug resistance. While Whole Genome Sequencing offers a more precise DR profile, the lack of user-friendly bioinformatics analysis applications hinders its widespread use. This review focuses on exploring various artificial intelligence models for predicting DR-TB profiles, analyzing relevant English-language articles using the PRISMA methodology through the Covidence platform. Our findings indicate that an Artificial Neural Network is the most commonly employed method, with non-statistical dimensionality reduction techniques preferred over traditional statistical approaches such as Principal Component Analysis or t-distributed Stochastic Neighbor Embedding.

New advances in the treatments of drug-resistant tuberculosis

INTRODUCTION

TB is associated with high mortality and morbidity among infected individuals and a high transmission rate from person to person. Despite the availability of vaccines and several anti-TB,TB infection continues to increase. Global resistance to TB remains the greatest challenge. There has not been extensive research into a new treatment and management strategy for TB resistance therapy. This review is based on a review of new advances and alternative drugs in the treatment of drug-resistant TB.

AREAS COVERED

New drug-resistant Mycobacterium tuberculosis therapy involves a combination of the latest TB drugs, new anti-TB drugs based on medicinal plant extracts for drug-resistant TB, mycobacteriophage therapy, the CRISPR/Cas9 system, and nanotechnology.

EXPERT OPINION

It is necessary to determine the function of individual gene alterations in drug-resistant TB. A combination of the most recent anti-TB drugs, such as bedaquiline and delamanid, is recommended. Longitudinal studies and animal model experiments with some medicinal plant extracts are required for better results. Nanotechnology has the potential to reduce drug side effects. Useful efficacy of phage therapy and CRISPR-cas9 technology as adjunct therapies for the management of drug-resistant TB.

Identification of a Specific Biomarker of Acinetobacter baumannii Global Clone 1 by Machine Learning and PCR Related to Metabolic Fitness of ESKAPE Pathogens

Since the emergence of high-risk clones worldwide, constant investigations have been undertaken to comprehend the molecular basis that led to their prevalent dissemination in nosocomial settings over time. So far, the complex and multifactorial genetic traits of this type of epidemic clones have allowed only the identification of biomarkers with low specificity. A machine learning algorithm was able to recognize unequivocally a biomarker for early and accurate detection of Acinetobacter baumannii global clone 1 (GC1), one of the most disseminated high-risk clones. A support vector machine model identified the U1 sequence with a length of 367 nucleotides that matched a fragment of the moaCB gene, which encodes the molybdenum cofactor biosynthesis C and B proteins. U1 differentiates specifically between A. baumannii GC1 and non-GC1 strains, becoming a suitable biomarker capable of being translated into clinical settings as a molecular typing method for early diagnosis based on PCR as shown here. Since the metabolic pathways of Mo enzymes have been recognized as putative therapeutic targets for ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) pathogens, our findings highlight that machine learning can also be useful in knowledge gaps of high-risk clones and provides noteworthy support to the literature to identify relevant nosocomial biomarkers for other multidrug-resistant high-risk clones. IMPORTANCE A. baumannii GC1 is an important high-risk clone that rapidly develops extreme drug resistance in the nosocomial niche. Furthermore, several strains have been identified worldwide in environmental samples, exacerbating the risk of human interactions. Early diagnosis is mandatory to limit its dissemination and to outline appropriate antibiotic stewardship schedules. A region with a length of 367 bp (U1) within the moaCB gene that is not subjected to lateral genetic transfer or to antibiotic pressures was successfully found by a support vector machine model that predicts A. baumannii GC1 strains. At the same time, research on the group of Mo enzymes proposed this metabolic pathway related to the superbug's metabolism as a potential future drug target site for ESKAPE pathogens due to its central role in bacterial fitness during infection. These findings confirm that machine learning used for the identification of biomarkers of high-risk lineages can also serve to identify putative novel therapeutic target sites.

Geographical classification of malaria parasites through applying machine learning to whole genome sequence data

Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) (P. falciparum: 750 k, P. vivax: 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities.

Transmission and Drug Resistance Genotype of Multidrug-Resistant or Rifampicin-Resistant Mycobacterium tuberculosis in Chongqing, China

Multidrug-resistant or rifampicin-resistant tuberculosis (MDR/RR-TB) is a global barrier for the Stop TB plan. To identify risk factors for treatment outcome and cluster transmission of MDR/RR-TB, whole-genome sequencing (WGS) data of isolates from patients of the Chongqing Tuberculosis Control Institute were used for phylogenetic classifications, resistance predictions, and cluster analysis. A total of 223 MDR/RR-TB cases were recorded between 1 January 2018 and 31 December 2020. Elderly patients and those with lung cavitation are at increased risk of death due to MDR/RR-TB. A total of 187 MDR/RR strains were obtained from WGS data; 152 were classified as lineage 2 strains. Eighty (42.8%) strains differing by a distance of 12 or fewer single nucleotide polymorphisms were classified as 20 genomic clusters, indicating recent transmission. Patients infected with lineage 2 strains or those with occupations listed as "other" are significantly associated with a transmission cluster of MDR/RR-TB. Analysis of resistant mutations against first-line tuberculosis drugs found that 76 (95.0%) of all 80 strains had the same mutations within each cluster. A total of 55.0% (44 of 80) of the MDR/RR-TB strains accumulated additional drug resistance mutations along the transmission chain, especially against fluoroquinolones (63.6% [28 of 44]). Recent transmission of MDR/RR strains is driving the MDR/RR-TB epidemics, leading to the accumulation of more serious resistance along the transmission chains. IMPORTANCE The drug resistance molecular characteristics of MDR/RR-TB were elucidated by genome-wide analysis, and risk factors for death by MDR/RR-TB were identified in combination with patient information. Cluster characteristics of MDR/RR-TB in the region were analyzed by genome-wide analysis, and risk factors for cluster transmission (recent transmission) were analyzed. These analyses provide reference for the prevention and treatment of MDR/RR-TB in Chongqing.

The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis

With the increasing incidence and mortality of pulmonary tuberculosis, in addition to tough and controversial disease management, time-wasting and resource-limited conventional approaches to the diagnosis and differential diagnosis of tuberculosis are still awkward issues, especially in countries with high tuberculosis burden and backwardness. In the meantime, the climbing proportion of drug-resistant tuberculosis poses a significant hazard to public health. Thus, auxiliary diagnostic tools with higher efficiency and accuracy are urgently required. Artificial intelligence (AI), which is not new but has recently grown in popularity, provides researchers with opportunities and technical underpinnings to develop novel, precise, rapid, and automated implements for pulmonary tuberculosis care, including but not limited to tuberculosis detection. In this review, we aimed to introduce representative AI methods, focusing on deep learning and radiomics, followed by definite descriptions of the state-of-the-art AI models developed using medical images and genetic data to detect pulmonary tuberculosis, distinguish the infection from other pulmonary diseases, and identify drug resistance of tuberculosis, with the purpose of assisting physicians in deciding the appropriate therapeutic schedule in the early stage of the disease. We also enumerated the challenges in maximizing the impact of AI in this field such as generalization and clinical utility of the deep learning models.

Machine learning in the loop for tuberculosis diagnosis support

The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited.

MycoVarP: Mycobacterium Variant and Drug Resistance Prediction Pipeline for Whole-Genome Sequence Data Analysis

Whole-genome sequencing (WGS) provides a comprehensive tool to analyze the bacterial genomes for genotype–phenotype correlations, diversity of single-nucleotide variant (SNV), and their evolution and transmission. Several online pipelines and standalone tools are available for WGS analysis of Mycobacterium tuberculosis (Mtb) complex (MTBC). While they facilitate the processing of WGS data with minimal user expertise, they are either too general, providing little insights into bacterium-specific issues such as gene variations, INDEL/synonymous/PE-PPE (IDP family), and drug resistance from sample data, or are limited to specific objectives, such as drug resistance. It is understood that drug resistance and lineage-specific issues require an elaborate prioritization of identified variants to choose the best target for subsequent therapeutic intervention. Mycobacterium variant pipeline (MycoVarP) addresses these specific issues with a flexible battery of user-defined and default filters. It provides an end-to-end solution for WGS analysis of Mtb variants from the raw reads and performs two quality checks, viz, before trimming and after alignments of reads to the reference genome. MycoVarP maps the annotated variants to the drug-susceptible (DS) database and removes the false-positive variants, provides lineage identification, and predicts potential drug resistance. We have re-analyzed the WGS data reported by Advani et al. (2019) using MycoVarP and identified some additional variants not reported so far. We conclude that MycoVarP will help in identifying nonsynonymous, true-positive, drug resistance–associated variants more effectively and comprehensively, including those within the IDP of the PE-PPE/PGRS family, than possible from the currently available pipelines.

Characterisation of drug-resistant Mycobacterium tuberculosis mutations and transmission in Pakistan

Tuberculosis, caused by Mycobacterium tuberculosis, is a high-burden disease in Pakistan, with multi-drug (MDR) and extensive-drug (XDR) resistance, complicating infection control. Whole genome sequencing (WGS) of M. tuberculosis is being used to infer lineages (strain-types), drug resistance mutations, and transmission patterns-all informing infection control and clinical decision making. Here we analyse WGS data on 535 M. tuberculosis isolates sourced across Pakistan between years 2003 and 2020, to understand the circulating strain-types and mutations related to 12 anti-TB drugs, as well as identify transmission clusters. Most isolates belonged to lineage 3 (n = 397; 74.2%) strain-types, and were MDR (n = 328; 61.3%) and (pre-)XDR (n = 113; 21.1%). By inferring close genomic relatedness between isolates (< 10-SNPs difference), there was evidence of M. tuberculosis transmission, with 55 clusters formed consisting of a total of 169 isolates. Three clusters consist of M. tuberculosis that are similar to isolates found outside of Pakistan. A genome-wide association analysis comparing 'transmitted' and 'non-transmitted' isolate groups, revealed the nusG gene as most significantly associated with a potential transmissible phenotype (P = 5.8 × 10^-10). Overall, our study provides important insights into M. tuberculosis genetic diversity and transmission in Pakistan, including providing information on circulating drug resistance mutations for monitoring activities and clinical decision making.

Characterization of Fluoroquinolone-Resistant and Multidrug-Resistant Mycobacterium tuberculosis Isolates Using Whole-Genome Sequencing in Tianjin, China

Objective

Methods

Results

Conclusion

Drug resistance prediction and resistance genes identification in Mycobacterium tuberculosis based on a hierarchical attentive neural network utilizing genome-wide variants

Prediction of antimicrobial resistance based on whole-genome sequencing data has attracted greater attention due to its rapidity and convenience. Numerous machine learning-based studies have used genetic variants to predict drug resistance in Mycobacterium tuberculosis (MTB), assuming that variants are homogeneous, and most of these studies, however, have ignored the essential correlation between variants and corresponding genes when encoding variants, and used a limited number of variants as prediction input. In this study, taking advantage of genome-wide variants for drug-resistance prediction and inspired by natural language processing, we summarize drug resistance prediction into document classification, in which variants are considered as words, mutated genes in an isolate as sentences, and an isolate as a document. We propose a novel hierarchical attentive neural network model (HANN) that helps discover drug resistance-related genes and variants and acquire more interpretable biological results. It captures the interaction among variants in a mutated gene as well as among mutated genes in an isolate. Our results show that for the four first-line drugs of isoniazid (INH), rifampicin (RIF), ethambutol (EMB) and pyrazinamide (PZA), the HANN achieves the optimal area under the ROC curve of 97.90, 99.05, 96.44 and 95.14% and the optimal sensitivity of 94.63, 96.31, 92.56 and 87.05%, respectively. In addition, without any domain knowledge, the model identifies drug resistance-related genes and variants consistent with those confirmed by previous studies, and more importantly, it discovers one more potential drug-resistance-related gene.

A modified decision tree approach to improve the prediction and mutation discovery for drug resistance in Mycobacterium tuberculosis

BACKGROUND

Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling.

RESULTS

We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%).

CONCLUSION

Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.

Tuberculosis drug resistance profiling based on machine learning: A literature review

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is one of the top 10 causes of death worldwide. Drug-resistant tuberculosis (DR-TB) poses a major threat to the World Health Organization's “End TB” strategy which has defined its target as the year 2035. In 2019, there were close to 0.5 million cases of DRTB, of which 78% were resistant to multiple TB drugs. The traditional culture-based drug susceptibility test (DST - the current gold standard) often takes multiple weeks and the necessary laboratory facilities are not readily available in low-income countries. Whole genome sequencing (WGS) technology is rapidly becoming an important tool in clinical and research applications including transmission detection or prediction of DR-TB. For the latter, many tools have recently been developed using curated database(s) of known resistance conferring mutations. However, documenting all the mutations and their effect is a time-taking and a continuous process and therefore Machine Learning (ML) techniques can be useful for predicting the presence of DR-TB based on WGS data. This can pave the way to an earlier detection of drug resistance and consequently more efficient treatment when compared to the traditional DST.

Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases

In the era of antimicrobial resistance, the prevalence of multidrug-resistant microorganisms that resist conventional antibiotic treatment has steadily increased. Thus, it is now unquestionable that infectious diseases are significant global burdens that urgently require innovative treatment strategies. Emerging studies have demonstrated that artificial intelligence (AI) can transform drug delivery to promote effective treatment of infectious diseases. In this review, we propose to evaluate the significance, essential principles, and popular tools of AI in drug delivery for infectious disease treatment. Specifically, we will focus on the achievements and key findings of current research, as well as the applications of AI on drug delivery throughout the whole antimicrobial treatment process, with an emphasis on drug development, treatment regimen optimization, drug delivery system and administration route design, and drug delivery outcome prediction. To that end, the challenges of AI in drug delivery for infectious disease treatments and their current solutions and future perspective will be presented and discussed.

Application of Computational Methods in Understanding Mutations in Mycobacterium tuberculosis Drug Resistance

The emergence of drug-resistant strains of Mycobacterium tuberculosis (Mtb) impedes the End TB Strategy by the World Health Organization aiming for zero deaths, disease, and suffering at the hands of tuberculosis (TB). Mutations within anti-TB drug targets play a major role in conferring drug resistance within Mtb; hence, computational methods and tools are being used to understand the mechanisms by which they facilitate drug resistance. In this article, computational techniques such as molecular docking and molecular dynamics are applied to explore point mutations and their roles in affecting binding affinities for anti-TB drugs, often times lowering the protein’s affinity for the drug. Advances and adoption of computational techniques, chemoinformatics, and bioinformatics in molecular biosciences and resources supporting machine learning techniques are in abundance, and this has seen a spike in its use to predict mutations in Mtb. This article highlights the importance of molecular modeling in deducing how point mutations in proteins confer resistance through destabilizing binding sites of drugs and effectively inhibiting the drug action.

Accelerating antibiotic discovery through artificial intelligence

By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and de novo molecular design. Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.

Trends of unsuccessful treatment outcomes and associated factors among tuberculosis patients in public hospitals of Bale Zone, Southeast Ethiopia: A 5-year retrospective study

INTRODUCTION

Tuberculosis (TB) is a curable disease caused by the tubercle bacillus and its treatment is designed to cure, interrupt transmission, and prevent drug resistance. These aims have not yet been achieved in many regions of the world, particularly in developing countries like Ethiopia. Thus, this study was designed to assess the trends of unsuccessful treatment outcomes and associated factors among patients with TB in two public hospitals in the Bale zone, southeast Ethiopia.

METHODS

A 5-year retrospective data among 1281 patients with TB who registered and started treatment (from July 2013 to June 2018/19) in two selected Bale zone hospitals was retrieved. Together with descriptive statistics, binomial and multinomial logistic regression modeling were carried out using STATA version 14 to estimate the odds ratio.

RESULTS

The overall unsuccessful TB treatment outcomes in this study was 10.4% and moderately decreased over the year of treatment (from 14.1% to 8.4%, x2 = 7.35, and p = 0.011). Approximately 34 (7.6%) of pulmonary positive and 34 (7.4%) of pulmonary negative TB patients had experienced treatment failure and death, respectively. The level of the hospital, patients with smear-negative and extrapulmonary, transferred in, aged, and human immunodeficiency virus status were found to have a statistically significant association with unsuccessful treatment outcomes of patients with TB.

CONCLUSION

In this study, approximately one-tenth of patients with TB had unsuccessful treatment outcomes that moderately declined over the year of treatment. Strengthening control efforts like counseling during the intensive and continual phases of treatment and scheduling home visits is recommended.

INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis

Motivation

Prediction of drug resistance and identification of its mechanisms in bacteria such as Mycobacterium tuberculosis, the etiological agent of tuberculosis, is a challenging problem. Solving this problem requires a transparent, accurate, and flexible predictive model. The methods currently used for this purpose rarely satisfy all of these criteria. On the one hand, approaches based on testing strains against a catalogue of previously identified mutations often yield poor predictive performance; on the other hand, machine learning techniques typically have higher predictive accuracy, but often lack interpretability and may learn patterns that produce accurate predictions for the wrong reasons. Current interpretable methods may either exhibit a lower accuracy or lack the flexibility needed to generalize them to previously unseen data.

Contribution

In this paper we propose a novel technique, inspired by group testing and Boolean compressed sensing, which yields highly accurate predictions, interpretable results, and is flexible enough to be optimized for various evaluation metrics at the same time.

Results

We test the predictive accuracy of our approach on five first-line and seven second-line antibiotics used for treating tuberculosis. We find that it has a higher or comparable accuracy to that of commonly used machine learning models, and is able to identify variants in genes with previously reported association to drug resistance. Our method is intrinsically interpretable, and can be customized for different evaluation metrics. Our implementation is available at github.com/hoomanzabeti/INGOT_DR and can be installed via The Python Package Index (Pypi) under ingotdr. This package is also compatible with most of the tools in the Scikit-learn machine learning library.

Deep learning prediction of attention-deficit hyperactivity disorder in African Americans by copy number variation

Current understanding of the underlying molecular network and mechanism for attention-deficit hyperactivity disorder (ADHD) is lacking and incomplete. Previous studies suggest that genomic structural variations play an important role in the pathogenesis of ADHD. For effective modeling, deep learning approaches have become a method of choice, with ability to predict the impact of genetic variations involving complicated mechanisms. In this study, we examined copy number variation in whole genome sequencing from 116 African Americans ADHD children and 408 African American controls. We divided the human genome into 150 regions, and the variation intensity in each region was applied as feature vectors for deep learning modeling to classify ADHD patients. The accuracy of deep learning for predicting ADHD diagnosis is consistently around 78% in a two-fold shuffle test, compared with ∼50% by traditional k-mean clustering methods. Additional whole genome sequencing data from 351 European Americans children, including 89 ADHD cases and 262 controls, were applied as independent validation using feature vectors obtained from the African American ethnicity analysis. The accuracy of ADHD labeling was lower in this setting (∼70-75%) but still above the results from traditional methods. The regions with highest weight overlapped with the previously reported ADHD-associated copy number variation regions, including genes such as GRM1 and GRM8, key drivers of metabotropic glutamate receptor signaling. A notable discovery is that structural variations in non-coding genomic (intronic/intergenic) regions show prediction weights that can be as high as prediction weight from variations in coding regions, results that were unexpected.

Molecular epidemiology and drug-resistance of tuberculosis in Luodian revealed by whole genome sequencing

In this study, we aimed to investigate the molecular epidemiology and drug-resistance profiles of tuberculosis (TB) in Luodian, an area with highest TB incidence and limited healthcare resources in Guizhou, China. The passive case finding strategy was used to identify suspected pulmonary TB with symptoms, and individuals with positive Mycobacterium tuberculosis (MTB) culture were enrolled from May 22, 2018 to April 21, 2019. All the 107 cases except three came from nine towns, including 55.1% from Longping and Bianyang. The phylogeny tree showed that 53.3% of strains were Lineage 2 (Beijing genotype), while 46.7% were Lineage 4 (Euro-American genotype). Among Lineage 2 strains, 66.7% were of "modern" Beijing type. Seven clusters with genomic distance within 12 SNPs were identified. The clusters included 14 strains, accounting for a clustering rate of 13.1%. The distance separating the clustered cases was between 2.1 and 71.0 km (Km), with an average paired distance of 21.8 Km (interquartile range, 2.8-38.0 Km). Based on the gene mutations associated with drug-resistance, we predicted that 4.8% of strains were resistant to isoniazid, 3.7% to rifampicin, and 3.7% to streptomycin; only one strain (0.9%) had multidrug resistance (MDR). This study found low drug-resistance rates in Luodian, and the sub-lineage of the "modern" Beijing branch has recent expansion in Luodian. This work may also serve as a genomic baseline to assess the evolution and spread of MTB in Guizhou.

Pseudomonas aeruginosa: a clinical and genomics update

Antimicrobial resistance (AMR) has become a global medical priority that needs urgent resolution. Pseudomonas aeruginosa is a versatile, adaptable bacterial species with widespread environmental occurrence, strong medical relevance, a diverse set of virulence genes and a multitude of intrinsic and possibly acquired antibiotic resistance traits. P. aeruginosa causes a wide variety of infections and has an epidemic-clonal population structure. Several of its dominant global clones have collected a wide variety of resistance genes rendering them multi-drug resistant (MDR) and particularly threatening groups of vulnerable individuals including surgical patients, immunocompromised patients, Caucasians suffering from cystic fibrosis (CF) and more. AMR and MDR especially are particularly problematic in P. aeruginosa significantly complicating successful antibiotic treatment. In addition, antimicrobial susceptibility testing (AST) of P. aeruginosa can be cumbersome due to its slow growth or the massive production of exopolysaccharides and other extracellular compounds. For that reason, phenotypic AST is progressively challenged by genotypic methods using whole genome sequences (WGS) and large-scale phenotype databases as a framework of reference. We here summarize the state of affairs and the quality level of WGS-based AST for P. aeruginosa mostly from clinical origin.

Early Drug Development and Evaluation of Putative Antitubercular Compounds in the -Omics Era

Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis. According to the WHO, the disease is one of the top 10 causes of death of people worldwide. Mycobacterium tuberculosis is an intracellular pathogen with an unusually thick, waxy cell wall and a complex life cycle. These factors, combined with M. tuberculosis ability to enter prolonged periods of latency, make the bacterium very difficult to eradicate. The standard treatment of TB requires 6-20months, depending on the drug susceptibility of the infecting strain. The need to take cocktails of antibiotics to treat tuberculosis effectively and the emergence of drug-resistant strains prompts the need to search for new antitubercular compounds. This review provides a perspective on how modern -omic technologies facilitate the drug discovery process for tuberculosis treatment. We discuss how methods of DNA and RNA sequencing, proteomics, and genetic manipulation of organisms increase our understanding of mechanisms of action of antibiotics and allow the evaluation of drugs. We explore the utility of mathematical modeling and modern computational analysis for the drug discovery process. Finally, we summarize how -omic technologies contribute to our understanding of the emergence of drug resistance.

Robust detection of point mutations involved in multidrug-resistant Mycobacterium tuberculosis in the presence of co-occurrent resistance markers

Tuberculosis disease is a major global public health concern and the growing prevalence of drug-resistant Mycobacterium tuberculosis is making disease control more difficult. However, the increasing application of whole-genome sequencing as a diagnostic tool is leading to the profiling of drug resistance to inform clinical practice and treatment decision making. Computational approaches for identifying established and novel resistance-conferring mutations in genomic data include genome-wide association study (GWAS) methodologies, tests for convergent evolution and machine learning techniques. These methods may be confounded by extensive co-occurrent resistance, where statistical models for a drug include unrelated mutations known to be causing resistance to other drugs. Here, we introduce a novel ‘cannibalistic’ elimination algorithm (“Hungry, Hungry SNPos”) that attempts to remove these co-occurrent resistant variants. Using an M. tuberculosis genomic dataset for the virulent Beijing strain-type (n = 3,574) with phenotypic resistance data across five drugs (isoniazid, rifampicin, ethambutol, pyrazinamide, and streptomycin), we demonstrate that this new approach is considerably more robust than traditional methods and detects resistance-associated variants too rare to be likely picked up by correlation-based techniques like GWAS.

Tuberculosis is one of the deadliest infectious diseases, being responsible for more than one million deaths per year. The causing bacteria are becoming increasingly drug-resistant, which is hampering disease control. At the same time, an unprecedented amount of bacterial whole-genome sequencing is increasingly informing clinical practice. In order to detect the genetic alterations responsible for developing drug resistance and predict resistance status from genomic data, bio-statistical methods and machine learning models have been employed. However, due to strongly overlapping drug resistance phenotypes and genotypes in multidrug-resistant datasets, the results of these correlation-based approaches frequently also contain mutations related to resistance against other drugs. In the past, this issue has often been ignored or partially resolved by either restricting the input data or in post-analysis screening—with both strategies relying on prior information. Here we present a heuristic algorithm for finding resistance-associated variants and demonstrate that it is considerably more robust towards co-occurrent resistance compared to traditional techniques. The software is available at https://github.com/julibeg/HHS.

Combining structure and genomics to understand antimicrobial resistance

Antimicrobials against bacterial, viral and parasitic pathogens have transformed human and animal health. Nevertheless, their widespread use (and misuse) has led to the emergence of antimicrobial resistance (AMR) which poses a potentially catastrophic threat to public health and animal husbandry. There are several routes, both intrinsic and acquired, by which AMR can develop. One major route is through non-synonymous single nucleotide polymorphisms (nsSNPs) in coding regions. Large scale genomic studies using high-throughput sequencing data have provided powerful new ways to rapidly detect and respond to such genetic mutations linked to AMR. However, these studies are limited in their mechanistic insight. Computational tools can rapidly and inexpensively evaluate the effect of mutations on protein function and evolution. Subsequent insights can then inform experimental studies, and direct existing or new computational methods. Here we review a range of sequence and structure-based computational tools, focussing on tools successfully used to investigate mutational effect on drug targets in clinically important pathogens, particularly Mycobacterium tuberculosis. Combining genomic results with the biophysical effects of mutations can help reveal the molecular basis and consequences of resistance development. Furthermore, we summarise how the application of such a mechanistic understanding of drug resistance can be applied to limit the impact of AMR.

Amino Acid k-mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights

Machine learning algorithms can learn mechanisms of antimicrobial resistance from the data of DNA sequence without any a priori information. Interpreting a trained machine learning algorithm can be exploited for validating the model and obtaining new information about resistance mechanisms. Different feature extraction methods, such as SNP calling and counting nucleotide k-mers have been proposed for presenting DNA sequences to the model. However, there are trade-offs between interpretability, computational complexity and accuracy for different feature extraction methods. In this study, we have proposed a new feature extraction method, counting amino acid k-mers or oligopeptides, which provides easier model interpretation compared to counting nucleotide k-mers and reaches the same or even better accuracy in comparison with different methods. Additionally, we have trained machine learning algorithms using different feature extraction methods and compared the results in terms of accuracy, model interpretability and computational complexity. We have built a new feature selection pipeline for extraction of important features so that new AMR determinants can be discovered by analyzing these features. This pipeline allows the construction of models that only use a small number of features and can predict resistance accurately.

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/ .

Recent evolutions of machine learning applications in clinical laboratory medicine

Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

Multi-Label Random Forest Model for Tuberculosis Drug Resistance Classification and Mutation Ranking

Resistance prediction and mutation ranking are important tasks in the analysis of Tuberculosis sequence data. Due to standard regimens for the use of first-line antibiotics, resistance co-occurrence, in which samples are resistant to multiple drugs, is common. Analysing all drugs simultaneously should therefore enable patterns reflecting resistance co-occurrence to be exploited for resistance prediction. Here, multi-label random forest (MLRF) models are compared with single-label random forest (SLRF) for both predicting phenotypic resistance from whole genome sequences and identifying important mutations for better prediction of four first-line drugs in a dataset of 13402 Mycobacterium tuberculosis isolates. Results confirmed that MLRFs can improve performance compared to conventional clinical methods (by 18.10%) and SLRFs (by 0.91%). In addition, we identified a list of candidate mutations that are important for resistance prediction or that are related to resistance co-occurrence. Moreover, we found that retraining our analysis to a subset of top-ranked mutations was sufficient to achieve satisfactory performance. The source code can be found at http://www.robots.ox.ac.uk/~davidc/code.php.